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Graph enhanced spatial-temporal transformer for traffic flow forecasting
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AI
DOI：10.1016/j.asoc.2025.112698.png)
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Abstract
En 中文
Traffic flow forecasting, which aims to predict future traffic patterns based on current conditions, is a crucial yet challenging task in intelligent transportation systems due to the complex spatial-temporal relationships involved. Existing methods often struggle to effectively capture these intricate spatial dependencies and temporal patterns. To address these limitations, we propose a graph enhanced spatial-temporal Transformer (GE-STT), which integrates a graph enhanced module and a spatial-temporal Transformer module for improved prediction accuracy. Specifically, the graph enhanced module combines a Graph Convolutional Network (GCN) with a Gated Recurrent Unit (GRU) to obtain enriched spatial-temporal features, and introduce the original traffic data as a correction term to deal with the errors in the enhancement process. The spatial-temporal Transformer then leverages these enhanced features for final prediction. Experimental results on four traffic datasets show that GE-STT achieves superior performance under various metrics. Compared with the best baseline in different datasets, the performance of GE-STT is improved by up to 8% under the MAE metric, highlighting its robustness and effectiveness in traffic flow forecasting tasks.
Keywords:
Spatial-temporal transformer
Graph convolutional network
Traffic flow forecasting
intelligent transportation systems
Spatial-temporal modeling
Journal
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